This notebook is part of my final project for the Music Information Retrieval course of the Master in Sound and Music Computing from Universitat Pompeu Fabra in Barcelona. The report accompanying the notebook can be found here.

Multi-task learning can improve the generalization and robustness of data-driven models, assuming a statistical relationships between the different tasks. Recent approaches to automatic drum transcription (ADT) have exploited the locally periodic nature of Western drum performances by training ADT systems jointly with the task of beat and downbeat detection. While most ADT vocabularies contain only the three most common drum instruments (kick-drum, snare-drum and hi-hat), training such models for larger vocabulary transcriptions encounters the difficulty that the available datasets do not offer a significant frequency of appearance of these less usual drum instruments. A recent approach by Cartwright and and Bello trains a CRNN model with a synthetic dataset along with other existing datasets in order to balance the instrument occurrences. In this project, I compare the performance of the multi-task version of this CRNN model and its single-task version on the Groove MIDI Dataset.

The jupyter notebook uses a conda environment that can be easily set up by running the following commands in the terminal:

CONDA_RESTORE_FREE_CHANNEL=1 conda env create -f environment.yml

and then:

conda activate rhythm